Abstract: BACKGROUND: Factors such as infection, limb ischemia, and histiocyte activation are involved in diabetic foot ulcers, but the key cell subpopulations influencing diabetic foot ulcer healing remain unclear, and specific biomarkers for diabetic foot ulcers have yet to be identified. Gene Expression Omnibus (GEO) is a publicly accessible database managed by the National Center for Biotechnology Information (NCBI) that stores high-throughput gene expression data, allowing users to freely submit, share, query, and analyze data. By conducting secondary analysis on published data, research costs can be minimized and new research avenues and methods can be discovered.
OBJECTIVE: To screen for biomarkers for diabetic foot ulcers using single-cell transcriptomics and conventional transcriptomics bioinformatics analysis, high-dimensional weighted gene co-expression network analysis, and weighted gene co-expression network analysis.
METHODS: The single-cell transcriptomics dataset GSE165816 from the gene expression database was downloaded, including unhealed ulcer tissue samples from diabetic foot ulcer patients and foot skin samples from diabetic patients. After data quality control, dimensionality reduction, differential analysis, cell type annotation, and pseudotime analysis, cell types throughout the entire course of diabetic foot ulcers were identified, differentially expressed genes were obtained, and high-dimensional weighted gene co-expression network analysis was used to identify gene modules highly associated with diabetic foot ulcers. The conventional transcriptomics datasets GSE68183 and GSE80178, including unhealed ulcer tissue samples from diabetic foot ulcer patients and foot skin samples from diabetic patients, were downloaded to screen for differentially expressed genes. Weighted gene co-expression network analysis was used to screen for gene modules associated with diabetic foot ulcers. The differentially expressed genes from single-cell transcriptomics, conventional transcriptomics samples, weighted gene co-expression network analysis, and high-dimensional weighted gene co-expression network analysis were integrated to screen for biomarkers of diabetic foot ulcers. The GSE134431 dataset was downloaded as a validation conventional transcriptomics dataset to compare the expression levels of diabetic foot ulcer biomarkers in single-cell transcriptomics and validation conventional transcriptomics datasets. Wound tissues were obtained from diabetic and diabetic foot ulcer rat models to detect biomarker expression levels using immunohistochemistry and Western blot assays.
RESULTS AND CONCLUSION: Single-cell transcriptomics data analysis revealed that epithelial cell differentiation ran through the entire pathological process of diabetic foot ulcers. A total of 146 differentially expressed genes were identified, including 59 upregulated genes and 87 downregulated genes. High-dimensional weighted gene co-expression network analysis identified 19 gene modules associated with diabetic foot ulcers, containing 476 core genes. Conventional transcriptomics data analysis identified 913 differentially expressed genes, including 343 upregulated genes and 570 downregulated genes; weighted gene co-expression network analysis identified 19 gene modules associated with diabetic foot ulcers, containing 887 genes. Two biomarkers for diabetic foot ulcers were identified: S100A14 and SFN. The expression levels of these two genes were higher in diabetic foot ulcer samples from single-cell transcriptomics and conventional transcriptomics validation datasets compared with diabetic patients' foot skin samples. Animal experiments showed that the expression levels of S100A14 and SFN in wound tissue from diabetic foot ulcer rats were higher than those in back skin tissue from diabetic rats. These findings suggest that epithelial cells play a crucial role in the pathological process of diabetic foot ulcers, with S100A14 and SFN highly expressed in these ulcers, indicating that they could be potential targets for the treatment of diabetic foot ulcer.

Key words: diabetic foot ulcer, single-cell sequencing, weighted gene co-expression network analysis, biomarkers, differentially expressed genes